We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across $>$110 cm$^2$ areas over multi-millimeter axial ranges. Our computational microscope, termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope), features a parallelized, 54-camera architecture with 3-axis translation to capture, for each sample of interest, a multi-dimensional, 2.1-terabyte (TB) dataset, consisting of a total of 224,640 9.4-megapixel images. We developed a self-supervised neural network-based algorithm for 3D reconstruction and stitching that jointly estimates an all-in-focus photometric composite and 3D height map across the entire field of view, using multi-view stereo information and image sharpness as a focal metric. The memory-efficient, compressed differentiable representation offered by the neural network effectively enables joint participation of the entire multi-TB dataset during the reconstruction process. To demonstrate the broad utility of our new computational microscope, we applied STARCAM to a variety of decimeter-scale objects, with applications ranging from cultural heritage to industrial inspection.

翻译：我们提出一种大规模计算三维形貌显微镜，能够以微米级分辨率在>110 平方厘米区域、多毫米轴向范围内实现6吉像素轮廓测量三维成像。该计算显微镜命名为STARCAM（扫描式全聚焦重构计算阵列显微镜），采用54台相机并行架构并配备三轴平移系统，每份目标样品可采集由总计224,640张9.4兆像素图像构成的多维2.1太字节数据集。我们开发了基于自监督神经网络的算法进行三维重建与拼接，该算法利用多视角立体信息和图像清晰度作为焦度度量，联合估计全视野范围内的全聚焦光度合成图与三维高度图。神经网络提供的内存高效压缩可微分表示，有效实现了整个多太字节数据集在重建过程中的联合参与。为验证新型计算显微镜的广泛适用性，我们将STARCAM应用于从文化遗产到工业检测等多种分分米级尺度物体。